scholarly journals MainzelHandler: A Library for a Simple Integration and Usage of the Mainzelliste

2021 ◽  
Author(s):  
Daniel Preciado-Marquez ◽  
Ludger Becker ◽  
Michael Storck ◽  
Leonard Greulich ◽  
Martin Dugas ◽  
...  
Keyword(s):  
Medical Research ◽  
Open Source ◽  
Web Application ◽  
Vital Role ◽  
Medizinische Forschung ◽  
Open Source License ◽  
Personally Identifiable Information ◽  
Rest Api ◽  
Simple Integration

Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called “Mainzelliste”. This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste’s API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from https://github.com/DanielPreciado-Marquez/MainzelHandler.

MackDroid - An Android based Application to monitor devices

2016 ◽  
Vol 9 ◽  
pp. 1
Author(s):  
Maaz Sirkhot ◽  
Ekta Sirwani ◽  
Aishwarya Kourani ◽  
Akshit Batheja ◽  
Kajal Jethanand Jewani
Keyword(s):  
Operating System ◽  
Open Source ◽  
Vital Role ◽  
Mobile Users ◽  
Android Application ◽  
Web Page ◽  
Technological World ◽  
Monitoring Service ◽  
User Friendly

In this technological world, smartphones can be considered as one of the most far-reaching inventions. It plays a vital role in connecting people socially. The number of mobile users using an Android based smartphone has increased rapidly since last few years resulting in organizations, cyber cell departments, government authorities feeling the need to monitor the activities on certain targeted devices in order to maintain proper functionality of their respective jobs. Also with the advent of smartphones, Android became one of the most popular and widely used Operating System. Its highlighting features are that it is user friendly, smartly designed, flexible, highly customizable and supports latest technologies like IoT. One of the features that makes it exclusive is that it is based on Linux and is Open Source for all the developers. This is the reason why our project Mackdroid is an Android based application that collects data from the remote device, stores it and displays on a PHP based web page. It is primarily a monitoring service that analyzes the contents and distributes it in various categories like Call Logs, Chats, Key logs, etc. Our project aims at developing an Android application that can be used to track, monitor, store and grab data from the device and store it on a server which can be accessed by the handler of the application.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

<p>Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.</p><p>We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application.  </p><p>We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.</p><p>The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.</p>

MarkUs: An Open-Source Web Application to Annotate Student Papers On-Line

2012 ◽  
Author(s):  
Morgan Magnin ◽  
Guillaume Moreau ◽  
Nelle Varoquaux ◽  
Benjamin Vialle ◽  
Karen Reid ◽  
...  
Keyword(s):  
Higher Education ◽  
Open Source ◽  
Web Application ◽  
Online System ◽  
Important Benefit ◽  
Special Cases ◽  
Assessment Time ◽  
On Line ◽  
Active Pedagogy ◽  
Further Development

A critical component of the learning process lies in the feedback that students receive on their work that validates their progress, identifies flaws in their thinking, and identifies skills that still need to be learned. Many higher-education institutions have developed an active pedagogy that gives students opportunities for different forms of assessment and feedback. This means that students have numerous lab exercises, assignments, and projects. Both instructors and students thus require effective tools to efficiently manage the submission, assessment, and individualized feedback of students’ work. The open-source web application MarkUs aims at meeting these needs: it facilitates the submission and assessment of students’ work. Students directly submit their work using MarkUs, rather than printing it, or sending it by email. The instructors or teaching assistants use MarkUs’s interface to view the students’ work, annotate it, and fill in a marking rubric. Students use the same interface to read the annotations and learn from the assessment. Managing the students’ submissions and the instructors assessments within a single online system, has led to several positive pedagogical outcomes: the number of late submissions has decreased, the assessment time has been drastically reduced, students can access their results and read the instructor’s feedback immediately after the grading process is completed. Using MarkUs has also significantly reduced the time that instructors spend collecting assignments, creating the marking schemes, passing them on to graders, handling special cases, and returning work to the students. In this paper, we introduce MarkUs’ features, and illustrate their benefits for higher education through our own teaching experiences and that of our colleagues. We also describe an important benefit of the fact that the tool itself is open-source. MarkUs has been developed entirely by students giving them a valuable learning opportunity as they work on a large software system that real users depend on. Virtuous circles indeed arise, with former users of MarkUs becoming developers and then supervisors of further development. We will conclude by drawing perspectives about forthcoming features and use, both technically and pedagogically.

scholarly journals An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

2014 ◽  
Vol 102 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Torregrosa Daniel ◽  
Forcada Mikel L. ◽  
Pérez-Ortiz Juan Antonio
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Web Application ◽  
Statistical Machine Translation ◽  
Black Box ◽  
Translation System ◽  
Web Tool ◽  
Web Based ◽  
Strongly Coupled ◽  
Machine Translation System

Abstract We present a web-based open-source tool for interactive translation prediction (ITP) and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.

scholarly journals DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

2020 ◽  
Author(s):  
Wei Hao Khoong
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Preprocessing ◽  
Weighted Distance ◽  
Open Source License ◽  
Classification Tasks ◽  
Python Package

In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.

scholarly journals Das deutschlandweite, webbasierte ParaReg-Register zur lebenslangen Dokumentation von Querschnittgelähmten – Datenmodell, rechtlich-ethische Voraussetzungen und technische Implementierung

2021 ◽  
Vol 83 (S 01) ◽  
pp. S18-S26
Author(s):  
Rüdiger Rupp ◽  
Patrick Jersch ◽  
Christian Schuld ◽  
Joachim Schweidler ◽  
Nils-Hendrik Benning ◽  
...  
Keyword(s):  
Open Source ◽  
Seltene Erkrankungen ◽  
Medizinische Forschung

Abstract Ziel der Studie In Deutschland unterscheiden sich die Behandlungspfade von frisch Querschnittgelähmten in Abhängigkeit von intrinsisch-krankheitsspezifischen und extrinsischen Faktoren erheblich. Welche dieser Faktoren mit einem verbesserten Outcome, weniger Folgekomplikationen und stationären Wiederaufnahmen assoziiert sind, ist bis heute nicht bekannt. Daher soll das deutschlandweite, patientenzentrierte, webbasierte ParaReg-Register implementiert werden, um langfristig eine bessere Qualität der Patientenversorgung, Planung der Behandlungspfade und Kosteneffizienz zu erreichen. Methodik In der Konzeptionierungsphase 2017/18 wurde das Datenmodell des Registers vom ParaReg-Leitungskomitee in einem iterativen Prozess zusammen mit dem erweiterten Vorstand der Deutschsprachigen Medizinischen Gesellschaft für Paraplegiologie e.V. (DMGP) und Patientenvertretern entwickelt. In ParaReg werden soziale und medizinische Routinedaten zusammen mit international etablierten neurologischen, funktionellen und partizipationsbezogenen Scores dokumentiert. Die Vergabe einer eindeutigen Patienten-ID erlaubt lebenslang eine zentrumsübergreifende Dokumentation von stationären Aufenthalten in einem der 27 in Deutschland in der DMGP organisierten Querschnittzentren. Das ParaReg-Datenschutzkonzept und die Patienteninformation/-einwilligung orientieren sich an den um DSGVO-relevante Aspekte erweiterten Vorlagen des Open Source Registers für seltene Erkrankungen (OSSE). Ergebnisse In der 2019 begonnenen Realisierungsphase wurde die informationstechnische Infrastruktur gemäß des klinischen ID-Management-Moduls der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) umgesetzt. Parallel wurden die rechtlich-ethischen Voraussetzungen für den Registerbetrieb unter der Schirmherrschaft der DMGP geschaffen. In das Datenschutzkonzept sind die Empfehlungen der Arbeitsgruppe Datenschutz der TMF eingeflossen. Basierend auf den Rückmeldungen aus der Alpha-Testphase mit Eingabe der Aufenthaltsdaten von 40 Patienten wurde die Ergonomie der elektronischen Eingabeformulare speziell für mobile Eingabegeräte verbessert. Schlussfolgerung Mit Abschluss der monozentrischen Alpha-Testphase hat die multizentrische Datenerhebung an 5 DMGP-Querschnittzentren begonnen. Die Nachhaltigkeit von ParaReg ist durch die strukturelle und finanzielle Unterstützung durch die DMGP auch nach Auslaufen der Förderung durch das Bundesministerium für Bildung und Forschung (BMBF) gesichert.

scholarly journals Doclass: open-source software to support document labeling and classification

2020 ◽  
Author(s):  
Marcelo Inuzuka ◽  
Hugo Do Nascimento ◽  
Fernando Almeida ◽  
Bruno Barros ◽  
Walid Jradi
Keyword(s):  
Active Learning ◽  
Open Source ◽  
Open Source Software ◽  
Design Science ◽  
Text Processing ◽  
Science Research ◽  
Development Stage ◽  
Large Sets ◽  
Rest Api ◽  
Future Work

This article introduces Doclass, a free and open-source software for the Web that aims to assist in labeling and classifying large sets of documents. The research involved a design science research methodology, guided by the real demands of a legal text processing company. The architecture, several design decisions and the current development stage of the software are presented. Preliminary user experiments for evaluating interactive document labeling are described. As a result, the first version of a system with an architecture composed of a mobile frontend that communicates with a backend through a REST API was published, with satisfactory performance evaluation by the applicant. Other results involve the use of active learning techniques to reduce human effort when performing the classification of documents, as well as the Uncertainty strategy to choose the document to be labeled. The effectiveness of the stop criterion for the active learning technique based on confidence level was tested and proved unsatisfactory, remaining as a future work.

scholarly journals ATGme: Open-source web application for rare codon identification and custom DNA sequence optimization

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Edward Daniel ◽  
Goodluck U. Onwukwe ◽  
Rik K. Wierenga ◽  
Susan E. Quaggin ◽  
Seppo J. Vainio ◽  
...  
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Web Application ◽  
Rare Codon ◽  
Sequence Optimization
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